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Author SHA1 Message Date
Philip Meier
dd313d7338 support TestCase.longMessage in TestCase.assertEqual 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77602

Approved by: https://github.com/mruberry
 2022-05-20 11:09:28 +00:00
Pearu Peterson
8b5f11c61e Support copy_ for Sparse Compressed tensors. 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77605

Approved by: https://github.com/cpuhrsch
 2022-05-18 21:22:19 +00:00
Jane Xu
a325fa94b9 [flaky test reporting] print stack trace for flaky reruns (#77664) 
Give context about failures ~and include it in the test report~! Unfortunately I cannot include it easily in the test report through the addExpectedFailures :c as tracebacks are not something I can instantiate. (see revert comment)

Current way doesn't print the stack traces, which has been fine because we don't hide the signal and it shows up at the end:
<img width="545" alt="image" src="https://user-images.githubusercontent.com/31798555/168878182-914edc39-369f-40bc-8b35-9d5cd47a6b1c.png">

However, when we want to hide the signal, we'd like to print the stack traces for each failed attempt, as it won't be shown at the end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77664
Approved by: https://github.com/suo
 2022-05-18 14:17:16 +00:00
PyTorch MergeBot
b87e5f383b Revert "[flaky test reporting] print stack trace for flaky reruns (#77664)" 
This reverts commit 646a20c8fe.

Reverted https://github.com/pytorch/pytorch/pull/77664 on behalf of https://github.com/janeyx99
 2022-05-17 21:27:21 +00:00
Jane Xu
646a20c8fe [flaky test reporting] print stack trace for flaky reruns (#77664) 
Give context about failures and include it in the test report!

Current way doesn't print the stack traces, which has been fine because we don't hide the signal and it shows up at the end:
<img width="545" alt="image" src="https://user-images.githubusercontent.com/31798555/168878182-914edc39-369f-40bc-8b35-9d5cd47a6b1c.png">

However, when we want to hide the signal, we'd like to print the stack traces for each failed attempt, as it won't be shown at the end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77664
Approved by: https://github.com/suo
 2022-05-17 18:06:14 +00:00
Kulin Seth
f348b1b2b5 Add the Runtime components for MPS backend. (#76725) 
The PR adds the runtime components and few basic operations like copy, as_strided for MPS backend.

Current list of identified TODOs are:

-  https://github.com/pytorch/pytorch/issues/77176
- Unify the logic with CUDACachingAllocator and remove redundant code.
-  https://github.com/pytorch/pytorch/issues/77170
- Look into using C++ smart pointers where possible with ObjC code
- Use empty_strided_generic() to implement the `empty_strided_mps` code
- https://github.com/pytorch/pytorch/issues/77144
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76725
Approved by: https://github.com/albanD
 2022-05-11 17:19:45 +00:00
Nikita Vedeneev
00a1fb64bb Faster index_select for sparse COO tensors on CPU. (#72710) 
Fixes https://github.com/pytorch/pytorch/issues/72212.

This PR improves the previous algorithm in complexity. It also utilizes the structure of the problem and parallelizes computations when possible.

Benchmark results.

<details>

<summary>Testing script</summary>

```python
import torch
import math
from IPython import get_ipython
from itertools import product
import pickle
from torch.utils.benchmark import Timer, Compare

torch.manual_seed(13)
#torch.set_num_threads(1)
ipython = get_ipython()

index_sizes = (100, 1000, 10000)
# specifies (n, nnz)
problem_dims = (
    # n > nnz
    (10000, 100),
    (100000, 1000),
    (1000000, 10000),
    # n < nnz
    (10, 100),
    (10, 1000),
    (10, 10000),
    (100, 1000),
    (100, 10000),
    (1000, 10000),
    (1000, 100000),
    (1000, 1000000),
    #(1000000, 1000000000),
)

def f(t, d, index):
    s = torch_sparse.SparseTensor.from_torch_sparse_coo_tensor(t)
    ss = s.index_select(d, index)
    return ss.coo()

name = "PR"
results = []

for (n, nnz), m in product(problem_dims, index_sizes):
    for d in (0, 1):
        if nnz < n:
            shape = (n, n)
        else:
            shape = (n, nnz // n) if d == 0 else (nnz // n, n)
        nrows, ncols = shape
        rowidx = torch.randint(low=0, high=nrows, size=(nnz,))
        colidx = torch.randint(low=0, high=ncols, size=(nnz,))
        itemidx = torch.vstack((rowidx, colidx))
        xvalues = torch.randn(nnz)
        index = torch.randint(low=0, high=n, size=(m,))

        SparseX = torch.sparse_coo_tensor(itemidx, xvalues, size=shape).coalesce()
        smtp = "SparseX.index_select(d, index)"
        timer = Timer(smtp,
                      globals=globals(),
                      label="coo.index_select",
                      description=f"{name}: coo.index_select",
                      sub_label=f"n={n}, nnz={nnz}, index_len={m}, dim={d}",
                      num_threads=torch.get_num_threads())
        results.append(timer.blocked_autorange())

compare = Compare(results)
compare.trim_significant_figures()
compare.print()

with open(f"{name}_index_select.pickle", 'wb') as f:
    pickle.dump(results, f)

```

</details>

<details>

<summary>Gather results</summary>

```python
import pickle
from torch.utils.benchmark import Timer, Compare

files = [
        "PR",
        "torch_sparse",
        "master"
        ]

timers = []
for name in files:
    with open("{}_index_select.pickle".format(name), 'rb') as f:
        timers += pickle.load(f)

compare = Compare(timers)
compare.trim_significant_figures()
compare.print()

```

</details>

<details>

<summary>PR/torch_sparse/master runtime comparison</summary>

```
[----------------------------------- coo.index_select ----------------------------------]
                                                    |    PR   |  torch_sparse  |   master
32 threads: -----------------------------------------------------------------------------
      n=10000, nnz=100, index_len=100, dim=0        |     14  |        140     |       10
      n=10000, nnz=100, index_len=100, dim=1        |     14  |        200     |       10
      n=10000, nnz=100, index_len=1000, dim=0       |     30  |        180     |       38
      n=10000, nnz=100, index_len=1000, dim=1       |     34  |        240     |       38
      n=10000, nnz=100, index_len=10000, dim=0      |    278  |        460     |      330
      n=10000, nnz=100, index_len=10000, dim=1      |    275  |        516     |      330
      n=100000, nnz=1000, index_len=100, dim=0      |     16  |        290     |       31
      n=100000, nnz=1000, index_len=100, dim=1      |     26  |        390     |       31
      n=100000, nnz=1000, index_len=1000, dim=0     |     45  |        405     |      263
      n=100000, nnz=1000, index_len=1000, dim=1     |     73  |        500     |      261
      n=100000, nnz=1000, index_len=10000, dim=0    |    444  |        783     |     2570
      n=100000, nnz=1000, index_len=10000, dim=1    |    470  |        890     |     2590
      n=1000000, nnz=10000, index_len=100, dim=0    |     25  |       2400     |      270
      n=1000000, nnz=10000, index_len=100, dim=1    |    270  |       4000     |      269
      n=1000000, nnz=10000, index_len=1000, dim=0   |     74  |       2600     |     2620
      n=1000000, nnz=10000, index_len=1000, dim=1   |    464  |       3600     |     2640
      n=1000000, nnz=10000, index_len=10000, dim=0  |    635  |       3300     |    26400
      n=1000000, nnz=10000, index_len=10000, dim=1  |   1000  |       3960     |    26400
      n=10, nnz=100, index_len=100, dim=0           |     16  |        137     |       16
      n=10, nnz=100, index_len=100, dim=1           |     16  |        220     |       16
      n=10, nnz=100, index_len=1000, dim=0          |     63  |        238     |       81
      n=10, nnz=100, index_len=1000, dim=1          |     60  |        698     |       78
      n=10, nnz=100, index_len=10000, dim=0         |    480  |        940     |      862
      n=10, nnz=100, index_len=10000, dim=1         |    330  |       4930     |     1070
      n=10, nnz=1000, index_len=100, dim=0          |     60  |        200     |       73
      n=10, nnz=1000, index_len=100, dim=1          |     56  |        683     |       70
      n=10, nnz=1000, index_len=1000, dim=0         |    480  |        530     |     1050
      n=10, nnz=1000, index_len=1000, dim=1         |    330  |       4550     |     1368
      n=10, nnz=1000, index_len=10000, dim=0        |   3100  |       2900     |     9300
      n=10, nnz=1000, index_len=10000, dim=1        |   3400  |      46000     |     9100
      n=10, nnz=10000, index_len=100, dim=0         |    400  |        453     |      857
      n=10, nnz=10000, index_len=100, dim=1         |    400  |       4070     |     1730
      n=10, nnz=10000, index_len=1000, dim=0        |   2840  |       2600     |    13900
      n=10, nnz=10000, index_len=1000, dim=1        |   3700  |      40600     |    16000
      n=10, nnz=10000, index_len=10000, dim=0       |  83200  |      67400     |   160000
      n=10, nnz=10000, index_len=10000, dim=1       |  68000  |     528000     |   190000
      n=100, nnz=1000, index_len=100, dim=0         |     46  |        148     |       31
      n=100, nnz=1000, index_len=100, dim=1         |     45  |        242     |       37
      n=100, nnz=1000, index_len=1000, dim=0        |     68  |        248     |      240
      n=100, nnz=1000, index_len=1000, dim=1        |     66  |        755     |      290
      n=100, nnz=1000, index_len=10000, dim=0       |    370  |        802     |     2250
      n=100, nnz=1000, index_len=10000, dim=1       |    372  |       5430     |     2770
      n=100, nnz=10000, index_len=100, dim=0        |     82  |        210     |      224
      n=100, nnz=10000, index_len=100, dim=1        |     74  |        986     |      270
      n=100, nnz=10000, index_len=1000, dim=0       |    350  |        618     |     2600
      n=100, nnz=10000, index_len=1000, dim=1       |    370  |       4660     |     4560
      n=100, nnz=10000, index_len=10000, dim=0      |   3000  |       3400     |    41680
      n=100, nnz=10000, index_len=10000, dim=1      |   5000  |      47500     |    30400
      n=1000, nnz=10000, index_len=100, dim=0       |     71  |        160     |      185
      n=1000, nnz=10000, index_len=100, dim=1       |     64  |        516     |      190
      n=1000, nnz=10000, index_len=1000, dim=0      |    100  |        249     |     1740
      n=1000, nnz=10000, index_len=1000, dim=1      |     98  |       1030     |     1770
      n=1000, nnz=10000, index_len=10000, dim=0     |    600  |        808     |    18300
      n=1000, nnz=10000, index_len=10000, dim=1     |    663  |       5300     |    18500
      n=1000, nnz=100000, index_len=100, dim=0      |    160  |        258     |     1890
      n=1000, nnz=100000, index_len=100, dim=1      |    200  |       3620     |     2050
      n=1000, nnz=100000, index_len=1000, dim=0     |    500  |        580     |    18700
      n=1000, nnz=100000, index_len=1000, dim=1     |    640  |       7550     |    30000
      n=1000, nnz=100000, index_len=10000, dim=0    |   3400  |       3260     |   186000
      n=1000, nnz=100000, index_len=10000, dim=1    |   3600  |      49600     |   194000
      n=1000, nnz=1000000, index_len=100, dim=0     |    517  |        957     |    18700
      n=1000, nnz=1000000, index_len=100, dim=1     |    680  |      39600     |    37600
      n=1000, nnz=1000000, index_len=1000, dim=0    |   3600  |       4500     |   186000
      n=1000, nnz=1000000, index_len=1000, dim=1    |   5800  |      76400     |   190000
      n=1000, nnz=1000000, index_len=10000, dim=0   |  50000  |      67900     |  1800000
      n=1000, nnz=1000000, index_len=10000, dim=1   |  45000  |     570000     |  1900000

Times are in microseconds (us).

```

</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72710
Approved by: https://github.com/pearu, https://github.com/cpuhrsch
 2022-05-10 16:33:13 +00:00
PyTorch MergeBot
8d67972b14 Revert "Faster index_select for sparse COO tensors on CPU. (#72710)" 
This reverts commit ce3857e73c.

Reverted https://github.com/pytorch/pytorch/pull/72710 on behalf of https://github.com/malfet
 2022-05-10 14:43:05 +00:00
Nikita Vedeneev
ce3857e73c Faster index_select for sparse COO tensors on CPU. (#72710) 
Fixes https://github.com/pytorch/pytorch/issues/72212.

This PR improves the previous algorithm in complexity. It also utilizes the structure of the problem and parallelizes computations when possible.

Benchmark results.

<details>

<summary>Testing script</summary>

```python
import torch
import math
from IPython import get_ipython
from itertools import product
import pickle
from torch.utils.benchmark import Timer, Compare

torch.manual_seed(13)
#torch.set_num_threads(1)
ipython = get_ipython()

index_sizes = (100, 1000, 10000)
# specifies (n, nnz)
problem_dims = (
    # n > nnz
    (10000, 100),
    (100000, 1000),
    (1000000, 10000),
    # n < nnz
    (10, 100),
    (10, 1000),
    (10, 10000),
    (100, 1000),
    (100, 10000),
    (1000, 10000),
    (1000, 100000),
    (1000, 1000000),
    #(1000000, 1000000000),
)

def f(t, d, index):
    s = torch_sparse.SparseTensor.from_torch_sparse_coo_tensor(t)
    ss = s.index_select(d, index)
    return ss.coo()

name = "PR"
results = []

for (n, nnz), m in product(problem_dims, index_sizes):
    for d in (0, 1):
        if nnz < n:
            shape = (n, n)
        else:
            shape = (n, nnz // n) if d == 0 else (nnz // n, n)
        nrows, ncols = shape
        rowidx = torch.randint(low=0, high=nrows, size=(nnz,))
        colidx = torch.randint(low=0, high=ncols, size=(nnz,))
        itemidx = torch.vstack((rowidx, colidx))
        xvalues = torch.randn(nnz)
        index = torch.randint(low=0, high=n, size=(m,))

        SparseX = torch.sparse_coo_tensor(itemidx, xvalues, size=shape).coalesce()
        smtp = "SparseX.index_select(d, index)"
        timer = Timer(smtp,
                      globals=globals(),
                      label="coo.index_select",
                      description=f"{name}: coo.index_select",
                      sub_label=f"n={n}, nnz={nnz}, index_len={m}, dim={d}",
                      num_threads=torch.get_num_threads())
        results.append(timer.blocked_autorange())

compare = Compare(results)
compare.trim_significant_figures()
compare.print()

with open(f"{name}_index_select.pickle", 'wb') as f:
    pickle.dump(results, f)

```

</details>

<details>

<summary>Gather results</summary>

```python
import pickle
from torch.utils.benchmark import Timer, Compare

files = [
        "PR",
        "torch_sparse",
        "master"
        ]

timers = []
for name in files:
    with open("{}_index_select.pickle".format(name), 'rb') as f:
        timers += pickle.load(f)

compare = Compare(timers)
compare.trim_significant_figures()
compare.print()

```

</details>

<details>

<summary>PR/torch_sparse/master runtime comparison</summary>

```
[----------------------------------- coo.index_select ----------------------------------]
                                                    |    PR   |  torch_sparse  |   master
32 threads: -----------------------------------------------------------------------------
      n=10000, nnz=100, index_len=100, dim=0        |     14  |        140     |       10
      n=10000, nnz=100, index_len=100, dim=1        |     14  |        200     |       10
      n=10000, nnz=100, index_len=1000, dim=0       |     30  |        180     |       38
      n=10000, nnz=100, index_len=1000, dim=1       |     34  |        240     |       38
      n=10000, nnz=100, index_len=10000, dim=0      |    278  |        460     |      330
      n=10000, nnz=100, index_len=10000, dim=1      |    275  |        516     |      330
      n=100000, nnz=1000, index_len=100, dim=0      |     16  |        290     |       31
      n=100000, nnz=1000, index_len=100, dim=1      |     26  |        390     |       31
      n=100000, nnz=1000, index_len=1000, dim=0     |     45  |        405     |      263
      n=100000, nnz=1000, index_len=1000, dim=1     |     73  |        500     |      261
      n=100000, nnz=1000, index_len=10000, dim=0    |    444  |        783     |     2570
      n=100000, nnz=1000, index_len=10000, dim=1    |    470  |        890     |     2590
      n=1000000, nnz=10000, index_len=100, dim=0    |     25  |       2400     |      270
      n=1000000, nnz=10000, index_len=100, dim=1    |    270  |       4000     |      269
      n=1000000, nnz=10000, index_len=1000, dim=0   |     74  |       2600     |     2620
      n=1000000, nnz=10000, index_len=1000, dim=1   |    464  |       3600     |     2640
      n=1000000, nnz=10000, index_len=10000, dim=0  |    635  |       3300     |    26400
      n=1000000, nnz=10000, index_len=10000, dim=1  |   1000  |       3960     |    26400
      n=10, nnz=100, index_len=100, dim=0           |     16  |        137     |       16
      n=10, nnz=100, index_len=100, dim=1           |     16  |        220     |       16
      n=10, nnz=100, index_len=1000, dim=0          |     63  |        238     |       81
      n=10, nnz=100, index_len=1000, dim=1          |     60  |        698     |       78
      n=10, nnz=100, index_len=10000, dim=0         |    480  |        940     |      862
      n=10, nnz=100, index_len=10000, dim=1         |    330  |       4930     |     1070
      n=10, nnz=1000, index_len=100, dim=0          |     60  |        200     |       73
      n=10, nnz=1000, index_len=100, dim=1          |     56  |        683     |       70
      n=10, nnz=1000, index_len=1000, dim=0         |    480  |        530     |     1050
      n=10, nnz=1000, index_len=1000, dim=1         |    330  |       4550     |     1368
      n=10, nnz=1000, index_len=10000, dim=0        |   3100  |       2900     |     9300
      n=10, nnz=1000, index_len=10000, dim=1        |   3400  |      46000     |     9100
      n=10, nnz=10000, index_len=100, dim=0         |    400  |        453     |      857
      n=10, nnz=10000, index_len=100, dim=1         |    400  |       4070     |     1730
      n=10, nnz=10000, index_len=1000, dim=0        |   2840  |       2600     |    13900
      n=10, nnz=10000, index_len=1000, dim=1        |   3700  |      40600     |    16000
      n=10, nnz=10000, index_len=10000, dim=0       |  83200  |      67400     |   160000
      n=10, nnz=10000, index_len=10000, dim=1       |  68000  |     528000     |   190000
      n=100, nnz=1000, index_len=100, dim=0         |     46  |        148     |       31
      n=100, nnz=1000, index_len=100, dim=1         |     45  |        242     |       37
      n=100, nnz=1000, index_len=1000, dim=0        |     68  |        248     |      240
      n=100, nnz=1000, index_len=1000, dim=1        |     66  |        755     |      290
      n=100, nnz=1000, index_len=10000, dim=0       |    370  |        802     |     2250
      n=100, nnz=1000, index_len=10000, dim=1       |    372  |       5430     |     2770
      n=100, nnz=10000, index_len=100, dim=0        |     82  |        210     |      224
      n=100, nnz=10000, index_len=100, dim=1        |     74  |        986     |      270
      n=100, nnz=10000, index_len=1000, dim=0       |    350  |        618     |     2600
      n=100, nnz=10000, index_len=1000, dim=1       |    370  |       4660     |     4560
      n=100, nnz=10000, index_len=10000, dim=0      |   3000  |       3400     |    41680
      n=100, nnz=10000, index_len=10000, dim=1      |   5000  |      47500     |    30400
      n=1000, nnz=10000, index_len=100, dim=0       |     71  |        160     |      185
      n=1000, nnz=10000, index_len=100, dim=1       |     64  |        516     |      190
      n=1000, nnz=10000, index_len=1000, dim=0      |    100  |        249     |     1740
      n=1000, nnz=10000, index_len=1000, dim=1      |     98  |       1030     |     1770
      n=1000, nnz=10000, index_len=10000, dim=0     |    600  |        808     |    18300
      n=1000, nnz=10000, index_len=10000, dim=1     |    663  |       5300     |    18500
      n=1000, nnz=100000, index_len=100, dim=0      |    160  |        258     |     1890
      n=1000, nnz=100000, index_len=100, dim=1      |    200  |       3620     |     2050
      n=1000, nnz=100000, index_len=1000, dim=0     |    500  |        580     |    18700
      n=1000, nnz=100000, index_len=1000, dim=1     |    640  |       7550     |    30000
      n=1000, nnz=100000, index_len=10000, dim=0    |   3400  |       3260     |   186000
      n=1000, nnz=100000, index_len=10000, dim=1    |   3600  |      49600     |   194000
      n=1000, nnz=1000000, index_len=100, dim=0     |    517  |        957     |    18700
      n=1000, nnz=1000000, index_len=100, dim=1     |    680  |      39600     |    37600
      n=1000, nnz=1000000, index_len=1000, dim=0    |   3600  |       4500     |   186000
      n=1000, nnz=1000000, index_len=1000, dim=1    |   5800  |      76400     |   190000
      n=1000, nnz=1000000, index_len=10000, dim=0   |  50000  |      67900     |  1800000
      n=1000, nnz=1000000, index_len=10000, dim=1   |  45000  |     570000     |  1900000

Times are in microseconds (us).

```

</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72710
Approved by: https://github.com/pearu, https://github.com/cpuhrsch
 2022-05-09 19:59:39 +00:00
Pearu Peterson
436a7be059 Factory functions for sparse CSC, BSR, and BSC tensors 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76634

Tests for Sparse Compressed factory functions

Pull Request resolved: https://github.com/pytorch/pytorch/pull/76746

Approved by: https://github.com/cpuhrsch
 2022-05-04 03:30:41 +00:00
Nikita Shulga
8473173c36 Remove breakpad dependency 
This functionality does not seem to be used
and there are some requests to update dependency.

Add `third_party` to torch_cpu include directories if compiling with
Caffe2 support, as `caffe2/quantization/server/conv_dnnlowp_op.cc` depends on `third_party/fbgemm/src/RefImplementations.h`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
 2022-05-03 20:21:55 +00:00
Mike Ruberry
f6bbecf8b5 Adds python ref consistency test, elementwise unary reference inputs, and formats test files 
Per title.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76626
Approved by: https://github.com/ngimel
 2022-05-01 22:42:46 +00:00
Mike Ruberry
4048d4cdd2 [primTorch] Prototype tracer and elementwise unary reference opinfo class 
Adds a prototype tracer with no caching support and the `ElementwiseUnaryPythonRefInfo` class. A reference for `floor` is added to test the latter, and the elementwise binary reference inputs are extended to also return noncontiguous inputs. The SampleInput transform operation has been updated to return an actual SampleInput instead of a tuple to facilitate uniform handling of (transformed) SampleInputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76388
Approved by: https://github.com/ngimel
 2022-04-27 14:40:21 +00:00
Alban Desmaison
3d7abc0e55 Make -h work with run_test.py 
As per title.

### When running `python run_test.py -h`
It used to show:
- The general unittest parser help that we print via a second thread 35545d85dc/torch/testing/_internal/common_utils.py (L467-L470)
- The common_utils's parser help

<details><summary>Full result</summary>
<p>

```bash
$ python run_test.py -h
usage: run_test.py [-h] [-v] [-q] [--locals] [-f] [-c] [-b] [-k TESTNAMEPATTERNS] [tests [tests ...]]

positional arguments:
  tests                a list of any number of test modules, classes and test methods.

optional arguments:
  -h, --help           show this help message and exit
  -v, --verbose        Verbose output
  -q, --quiet          Quiet output
  --locals             Show local variables in tracebacks
  -f, --failfast       Stop on first fail or error
  -c, --catch          Catch Ctrl-C and display results so far
  -b, --buffer         Buffer stdout and stderr during tests
  -k TESTNAMEPATTERNS  Only run tests which match the given substring

Examples:
  run_test.py                           - run default set of tests
  run_test.py MyTestSuite               - run suite 'MyTestSuite'
  run_test.py MyTestCase.testSomething  - run MyTestCase.testSomething
  run_test.py MyTestCase                - run all 'test*' test methods
                                       in MyTestCase

usage: run_test.py [-h] [--subprocess] [--seed SEED] [--accept] [--jit_executor JIT_EXECUTOR] [--repeat REPEAT] [--test_bailouts]
                   [--save-xml [SAVE_XML]] [--discover-tests] [--log-suffix LOG_SUFFIX] [--run-parallel RUN_PARALLEL]
                   [--import-slow-tests [IMPORT_SLOW_TESTS]] [--import-disabled-tests [IMPORT_DISABLED_TESTS]]

optional arguments:
  -h, --help            show this help message and exit
  --subprocess          whether to run each test in a subprocess
  --seed SEED
  --accept
  --jit_executor JIT_EXECUTOR
  --repeat REPEAT
  --test_bailouts
  --save-xml [SAVE_XML]
  --discover-tests
  --log-suffix LOG_SUFFIX
  --run-parallel RUN_PARALLEL
  --import-slow-tests [IMPORT_SLOW_TESTS]
  --import-disabled-tests [IMPORT_DISABLED_TESTS]
```

</p>
</details>

It now prints:
- The general unittest parser help the same way. Should we remove this? We can't merge them unfortunately as inittest does not accept parent / does not expose the parser for us to take it as a parent.
- The combined common_utils + run_test parsers help

<details><summary>Full result</summary>
<p>

```bash
$ python run_test.py -h
usage: run_test.py [-h] [-v] [-q] [--locals] [-f] [-c] [-b] [-k TESTNAMEPATTERNS] [tests [tests ...]]

positional arguments:
  tests                a list of any number of test modules, classes and test methods.

optional arguments:
  -h, --help           show this help message and exit
  -v, --verbose        Verbose output
  -q, --quiet          Quiet output
  --locals             Show local variables in tracebacks
  -f, --failfast       Stop on first fail or error
  -c, --catch          Catch Ctrl-C and display results so far
  -b, --buffer         Buffer stdout and stderr during tests
  -k TESTNAMEPATTERNS  Only run tests which match the given substring

Examples:
  run_test.py                           - run default set of tests
  run_test.py MyTestSuite               - run suite 'MyTestSuite'
  run_test.py MyTestCase.testSomething  - run MyTestCase.testSomething
  run_test.py MyTestCase                - run all 'test*' test methods
                                       in MyTestCase

Ignoring disabled issues:  []
usage: run_test.py [-h] [--subprocess] [--seed SEED] [--accept] [--jit_executor JIT_EXECUTOR] [--repeat REPEAT] [--test_bailouts]
                   [--save-xml [SAVE_XML]] [--discover-tests] [--log-suffix LOG_SUFFIX] [--run-parallel RUN_PARALLEL]
                   [--import-slow-tests [IMPORT_SLOW_TESTS]] [--import-disabled-tests [IMPORT_DISABLED_TESTS]] [-v] [--jit]
                   [--distributed-tests] [-core] [-pt] [-c] [-i TESTS [TESTS ...]] [-x TESTS [TESTS ...]] [-f TESTS] [-l TESTS]
                   [--bring-to-front TESTS [TESTS ...]] [--ignore-win-blocklist] [--continue-through-error]
                   [--export-past-test-times [EXPORT_PAST_TEST_TIMES]] [--shard SHARD SHARD] [--exclude-jit-executor]
                   [--exclude-distributed-tests] [--run-specified-test-cases [RUN_SPECIFIED_TEST_CASES]]
                   [--use-specified-test-cases-by {include,bring-to-front}] [--dry-run]
                   [additional_unittest_args [additional_unittest_args ...]]

Run the PyTorch unit test suite

positional arguments:
  additional_unittest_args
                        additional arguments passed through to unittest, e.g., python run_test.py -i sparse -- TestSparse.test_factory_size_check

optional arguments:
  -h, --help            show this help message and exit
  --subprocess          whether to run each test in a subprocess
  --seed SEED
  --accept
  --jit_executor JIT_EXECUTOR
  --repeat REPEAT
  --test_bailouts
  --save-xml [SAVE_XML]
  --discover-tests
  --log-suffix LOG_SUFFIX
  --run-parallel RUN_PARALLEL
  --import-slow-tests [IMPORT_SLOW_TESTS]
  --import-disabled-tests [IMPORT_DISABLED_TESTS]
  -v, --verbose         print verbose information and test-by-test results
  --jit, --jit          run all jit tests
  --distributed-tests, --distributed-tests
                        run all distributed tests
  -core, --core         Only run core tests, or tests that validate PyTorch's ops, modules,and autograd. They are defined by CORE_TEST_LIST.
  -pt, --pytest         If true, use `pytest` to execute the tests. E.g., this runs TestTorch with pytest in verbose and coverage mode: python run_test.py -vci torch -pt
  -c, --coverage        enable coverage
  -i TESTS [TESTS ...], --include TESTS [TESTS ...]
                        select a set of tests to include (defaults to ALL tests). tests must be a part of the TESTS list defined in run_test.py
  -x TESTS [TESTS ...], --exclude TESTS [TESTS ...]
                        select a set of tests to exclude
  -f TESTS, --first TESTS
                        select the test to start from (excludes previous tests)
  -l TESTS, --last TESTS
                        select the last test to run (excludes following tests)
  --bring-to-front TESTS [TESTS ...]
                        select a set of tests to run first. This can be used in situations where you want to run all tests, but care more about some set, e.g. after making a change to a specific component
  --ignore-win-blocklist
                        always run blocklisted windows tests
  --continue-through-error
                        Runs the full test suite despite one of the tests failing
  --export-past-test-times [EXPORT_PAST_TEST_TIMES]
                        dumps test times from previous S3 stats into a file, format JSON
  --shard SHARD SHARD   runs a shard of the tests (taking into account other selections), e.g., --shard 2 3 will break up the selected tests into 3 shards and run the tests in the 2nd shard (the first number should not exceed the second)
  --exclude-jit-executor
                        exclude tests that are run for a specific jit config
  --exclude-distributed-tests
                        exclude distributed tests
  --run-specified-test-cases [RUN_SPECIFIED_TEST_CASES]
                        load specified test cases file dumped from previous OSS CI stats, format CSV.  If all test cases should run for a <test_module> please add a single row:
                         test_filename,test_case_name
                         ...
                         <test_module>,__all__
                         ...
                        how we use the stats will be based on option "--use-specified-test-cases-by".
  --use-specified-test-cases-by {include,bring-to-front}
                        used together with option "--run-specified-test-cases". When specified test case file is set, this option allows the user to control whether to only run the specified test modules or to simply bring the specified modules to front and also run the remaining modules. Note: regardless of this option, we will only run the specified test cases  within a specified test module. For unspecified test modules with the bring-to-front option, all test cases will be run, as one may expect.
  --dry-run             Only list the test that will run.

where TESTS is any of: benchmark_utils/test_benchmark_utils, distributed/_shard/sharded_optim/test_sharded_optim, distributed/_shard/sharded_tensor/ops/test_binary_cmp, distributed/_shard/sharded_tensor/ops/test_elementwise_ops, distributed/_shard/sharded_tensor/ops/test_embedding, distributed/_shard/sharded_tensor/ops/test_embedding_bag, distributed/_shard/sharded_tensor/ops/test_init, distributed/_shard/sharded_tensor/ops/test_linear, distributed/_shard/sharded_tensor/ops/test_math_ops, distributed/_shard/sharded_tensor/test_megatron_prototype, distributed/_shard/sharded_tensor/test_partial_tensor, distributed/_shard/sharded_tensor/test_sharded_tensor, distributed/_shard/sharded_tensor/test_sharded_tensor_reshard, distributed/_shard/sharding_spec/test_sharding_spec, distributed/_shard/test_replicated_tensor, distributed/algorithms/test_join, distributed/elastic/events/lib_test, distributed/elastic/metrics/api_test, distributed/elastic/multiprocessing/api_test, distributed/elastic/timer/api_test, distributed/elastic/timer/local_timer_example, distributed/elastic/timer/local_timer_test, distributed/elastic/utils/distributed_test, distributed/elastic/utils/logging_test, distributed/elastic/utils/util_test, distributed/fsdp/test_flatten_params_wrapper, distributed/fsdp/test_fsdp_apply, distributed/fsdp/test_fsdp_checkpoint, distributed/fsdp/test_fsdp_clip_grad_norm, distributed/fsdp/test_fsdp_comm, distributed/fsdp/test_fsdp_core, distributed/fsdp/test_fsdp_freezing_weights, distributed/fsdp/test_fsdp_grad_acc, distributed/fsdp/test_fsdp_ignored_modules, distributed/fsdp/test_fsdp_input, distributed/fsdp/test_fsdp_memory, distributed/fsdp/test_fsdp_mixed_precision, distributed/fsdp/test_fsdp_multiple_forward, distributed/fsdp/test_fsdp_multiple_wrapping, distributed/fsdp/test_fsdp_optim_state, distributed/fsdp/test_fsdp_overlap, distributed/fsdp/test_fsdp_pure_fp16, distributed/fsdp/test_fsdp_state_dict, distributed/fsdp/test_fsdp_summon_full_params, distributed/fsdp/test_fsdp_traversal, distributed/fsdp/test_fsdp_uneven, distributed/fsdp/test_shard_utils, distributed/fsdp/test_utils, distributed/fsdp/test_wrap, distributed/nn/jit/test_instantiator, distributed/optim/test_zero_redundancy_optimizer, distributed/pipeline/sync/skip/test_api, distributed/pipeline/sync/skip/test_gpipe, distributed/pipeline/sync/skip/test_inspect_skip_layout, distributed/pipeline/sync/skip/test_leak, distributed/pipeline/sync/skip/test_portal, distributed/pipeline/sync/skip/test_stash_pop, distributed/pipeline/sync/skip/test_tracker, distributed/pipeline/sync/skip/test_verify_skippables, distributed/pipeline/sync/test_balance, distributed/pipeline/sync/test_bugs, distributed/pipeline/sync/test_checkpoint, distributed/pipeline/sync/test_copy, distributed/pipeline/sync/test_deferred_batch_norm, distributed/pipeline/sync/test_dependency, distributed/pipeline/sync/test_inplace, distributed/pipeline/sync/test_microbatch, distributed/pipeline/sync/test_phony, distributed/pipeline/sync/test_pipe, distributed/pipeline/sync/test_pipeline, distributed/pipeline/sync/test_stream, distributed/pipeline/sync/test_transparency, distributed/pipeline/sync/test_worker, distributed/rpc/cuda/test_tensorpipe_agent, distributed/rpc/test_faulty_agent, distributed/rpc/test_tensorpipe_agent, distributed/test_c10d_common, distributed/test_c10d_gloo, distributed/test_c10d_nccl, distributed/test_c10d_spawn_gloo, distributed/test_c10d_spawn_nccl, distributed/test_data_parallel, distributed/test_distributed_spawn, distributed/test_launcher, distributed/test_nccl, distributed/test_pg_wrapper, distributed/test_store, distributions/test_constraints, distributions/test_distributions, lazy/test_bindings, lazy/test_extract_compiled_graph, lazy/test_ts_opinfo, test_ao_sparsity, test_autocast, test_autograd, test_binary_ufuncs, test_bundled_inputs, test_complex, test_cpp_api_parity, test_cpp_extensions_aot_ninja, test_cpp_extensions_aot_no_ninja, test_cpp_extensions_jit, test_cuda, test_cuda_primary_ctx, test_dataloader, test_datapipe, test_deploy, test_deploy, test_dispatch, test_expanded_weights, test_foreach, test_function_schema, test_functional_autograd_benchmark, test_functional_optim, test_functionalization, test_futures, test_fx, test_fx_experimental, test_hub, test_import_stats, test_indexing, test_jit, test_jit_autocast, test_jit_cuda_fuser, test_jit_disabled, test_jit_fuser_legacy, test_jit_fuser_te, test_jit_legacy, test_jit_profiling, test_license, test_linalg, test_logging, test_masked, test_mkldnn, test_mobile_optimizer, test_model_dump, test_module_init, test_modules, test_monitor, test_multiprocessing, test_multiprocessing_spawn, test_namedtensor, test_namedtuple_return_api, test_native_functions, test_nestedtensor, test_nn, test_numba_integration, test_numpy_interop, test_openmp, test_ops, test_ops_gradients, test_ops_jit, test_optim, test_overrides, test_package, test_per_overload_api, test_profiler, test_pruning_op, test_public_bindings, test_python_dispatch, test_pytree, test_quantization, test_reductions, test_scatter_gather_ops, test_serialization, test_set_default_mobile_cpu_allocator, test_shape_ops, test_show_pickle, test_sort_and_select, test_sparse, test_sparse_csr, test_spectral_ops, test_stateless, test_tensor_creation_ops, test_tensorboard, test_tensorexpr, test_tensorexpr_pybind, test_testing, test_torch, test_type_hints, test_type_info, test_type_promotion, test_unary_ufuncs, test_utils, test_view_ops, test_vmap, test_vulkan, test_xnnpack_integration
```

</p>
</details>

### When running anything else (for example  `python test_autograd.py -h`)
It did not change and still does:
- The general unittest parser help that we print via a second thread
- The common_utils's parser help
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76152
Approved by: https://github.com/malfet, https://github.com/seemethere
 2022-04-25 14:01:33 +00:00
Thiago Crepaldi
90d31cb311 Emit ATen ops when symbolics raise + minor fixes 
Currently `torch.onnx.export(.., operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)` only issues ATen ops through explicit requests (e.g. `g.at()`) calls inside each op symbolic function. This is done based on specific conditions such as `operator_export_type==OperatorExportTypes.ONNX_ATEN_FALLBACK)` or `is_caffe2_aten_fallback()`

This PR extends the ATen fallback mechanism for scenarios when the symbolic function raises `RuntimeError` during export. The idea is that partial implementation of existing ONNX ops can fallback to ATen as a last resort. That is valuable because each operator can have many input combinations and not all are always implemented.

A minor fix was done to make sure the `overload_name` attribute is added to explicit ATen op fallback requests when a symbolic is not registered to a particular op.

ps: The behavior for builds with BUILD_CAFFE2=1 is not changed to ensure BC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74759
Approved by: https://github.com/garymm, https://github.com/msaroufim
 2022-04-23 21:24:25 +00:00
Thiago Crepaldi
e07134092f Add warning when importing caffe2 on build without BUILD_CAFFE2=1 
Confusing backtraces are issued to users when they run Caffe2 scripts (or tests) on PyTorch builds without Caffe2 enabled through `BUILD_CAFFE2=1`

This PR adds warnings (in more than one place) to return a friendly message for the user, helping them to overcome the problem by themselves

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73770
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm
 2022-04-21 12:28:10 +00:00
Edward Z. Yang
ee955b8bb9 Cannibalize noarch CI job into crossref CI job 
crossref is a new strategy for performing tests when you want
to run a normal PyTorch API call, separately run some variation of
the API call (e.g., same thing but all the arguments are meta tensors)
and then cross-reference the results to see that they are consistent.
Any logic you add to CrossRefMode will get run on *every* PyTorch API
call that is called in the course of PyTorch's test suite.  This can
be a good choice for correctness testing if OpInfo testing is not
exhaustive enough.

For now, the crossref test doesn't do anything except verify that
we can validly push a mode onto the torch function mode stack for all
functions.

Signed-off-by: Edward Z. Yang <ezyangfb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75988

Approved by: https://github.com/seemethere
 2022-04-20 11:56:25 +00:00
Edward Z. Yang
28a3654668 Make PYTORCH_TEST_WITH_SLOW_GRADCHECK consistent with other test envvars 
Signed-off-by: Edward Z. Yang <ezyangfb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75987

Approved by: https://github.com/soulitzer
 2022-04-19 01:13:08 +00:00
Edward Z. Yang
30943d1610 Remove noarchTest decorator 
These tests are cheap so it doesn't matter if we run them on all
configs.  This is in preparation for removing the noarch build
configuration entirely.

Signed-off-by: Edward Z. Yang <ezyangfb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75985

Approved by: https://github.com/seemethere, https://github.com/cbalioglu
 2022-04-19 00:48:49 +00:00
PyTorch MergeBot
d79d9fa283 Revert "Remove breakpad dependency" 
This reverts commit 9aa3c7fd83.

Reverted https://github.com/pytorch/pytorch/pull/75394 on behalf of https://github.com/malfet
 2022-04-17 17:58:51 +00:00
Nikita Shulga
9aa3c7fd83 Remove breakpad dependency 
This functionality does not seem to be used
and there are some requests to update dependency

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
 2022-04-17 17:43:45 +00:00
Thiago Crepaldi
9bbe1d632e Fix ONNX ATen fallback for non-caffe2 engines 
This PR introduces 3 BC changes:

First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.

Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.

Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
 2022-04-14 23:18:45 +00:00
Ivan Yashchuk
c7ae23b50e Extend CSR constructor to support batched indices and values 
This is the first portion of changes required to enable Batched CSR format described in https://github.com/pytorch/pytorch/issues/60854#batched-CSR-computation.

Currently, only the same batch shape for indices and values is allowed. In the future, we could enable "broadcasting" of indices and batched values, as done in xFormers (dd96b8d8be/xformers/components/attention/_sputnik_sparse.py (L441)).

This PR adds possibility to construct a batched CSR matrix with `torch.sparse_csr_tensor` and this batched CSR can be converted to a dense tensor with a `.to_dense()` call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74542
Approved by: https://github.com/cpuhrsch
 2022-04-07 17:10:52 +00:00
PyTorch MergeBot
6d832a7a20 Revert "Extend CSR constructor to support batched indices and values" 
This reverts commit eead599039.

Reverted https://github.com/pytorch/pytorch/pull/74542 on behalf of https://github.com/b0noI
 2022-04-05 21:39:34 +00:00
Ivan Yashchuk
eead599039 Extend CSR constructor to support batched indices and values 
This is the first portion of changes required to enable Batched CSR format described in https://github.com/pytorch/pytorch/issues/60854#batched-CSR-computation.

Currently, only the same batch shape for indices and values is allowed. In the future, we could enable "broadcasting" of indices and batched values, as done in xFormers (dd96b8d8be/xformers/components/attention/_sputnik_sparse.py (L441)).

This PR adds possibility to construct a batched CSR matrix with `torch.sparse_csr_tensor` and this batched CSR can be converted to a dense tensor with a `.to_dense()` call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74542
Approved by: https://github.com/cpuhrsch
 2022-04-04 22:09:44 +00:00
PyTorch MergeBot
cc23725e89 Revert "Extend CSR constructor to support batched indices and values" 
This reverts commit c074a53002.

Reverted https://github.com/pytorch/pytorch/pull/74542 on behalf of https://github.com/malfet
 2022-03-30 19:54:26 +00:00
Ivan Yashchuk
c074a53002 Extend CSR constructor to support batched indices and values 
This is the first portion of changes required to enable Batched CSR format described in https://github.com/pytorch/pytorch/issues/60854#batched-CSR-computation.

Currently, only the same batch shape for indices and values is allowed. In the future, we could enable "broadcasting" of indices and batched values, as done in xFormers (dd96b8d8be/xformers/components/attention/_sputnik_sparse.py (L441)).

This PR adds possibility to construct a batched CSR matrix with `torch.sparse_csr_tensor` and this batched CSR can be converted to a dense tensor with a `.to_dense()` call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74542
Approved by: https://github.com/cpuhrsch
 2022-03-29 21:20:25 +00:00
Elias Ellison
6694fdaccd Clean up profiling mode and profiling executor strategy (#73875) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875

Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.

The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.

The tests here are failing but get fixed with the PR above it, so i'll squash for landing.

Test Plan: Imported from OSS

Reviewed By: cpuhrsch

Differential Revision: D34938130

Pulled By: eellison

fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
 2022-03-29 18:38:51 +00:00
Christian Puhrsch
e55b73d65a Add strided layout support for to_dense 
Fixes #59958

Pull Request resolved: https://github.com/pytorch/pytorch/pull/74486
Approved by: https://github.com/pearu, https://github.com/suo
 2022-03-29 00:12:48 +00:00
Tristan Rice
5b915e844c c10d: retry dns lookup failures (#74641) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74641

This makes dns hostname lookup failures retryable since in some environments such as Kubernetes they're not guaranteed to be resolvable until the job starts. Retrying this eliminates the race condition.

This also fixes `sandcastle_skip_if` when used on the class instead of the method. Previously they wouldn't inherit from TestCase so just wouldn't run under buck at all.

Fixes https://github.com/pytorch/pytorch/issues/73682

Test Plan:
Added a unit test

```
buck test //caffe2/test/distributed:test_store
```

Reviewed By: aivanou

Differential Revision: D35092284

fbshipit-source-id: d40bf187e52c41f551e4fe41c536b2b0015588ee
(cherry picked from commit f8908309d8ee64c25ee466a6b4922f34f2b7618e)
 2022-03-24 19:51:09 +00:00
Maksim Dmitriyevich Podkorytov
6a664481d5 Print reason for test skipped in CI (#74451) 
Summary:
Issue: https://github.com/pytorch/pytorch/issues/69014 (skip reason is not printed for tests running in CI)

Cause: locally tests are fired with testrunner that is built into unittest. When IN_CI env variable is true - tests are fired with xml test runner from unittest-xml-reporting. They have different summary printing properties

Solution: patching printing logic in unittest-xml-reporting when a test is skipped

Pull Request resolved: https://github.com/pytorch/pytorch/pull/74451

Test Plan: examine CI run

Reviewed By: janeyx99

Differential Revision: D35050675

Pulled By: tenpercent

fbshipit-source-id: e3421687d68932079ba156153abeefee368b2fb3
(cherry picked from commit e2356d8ecc618dad9c7c556d40b47c1a6c53f68a)
 2022-03-23 00:02:26 +00:00
Mike Ruberry
0aa3c39e5f Extends OpInfo architecture with reference inputs, adds them for elementwise binary operators 
This PR extends our OpInfo test architecture with "reference inputs," an optional expansion of typical sample inputs that allows for more thorough testing. Currently only the elementwise binary operations implement an extended set of reference inputs. This PR also cleans up some smaller OpInfo-related issues, including several bugs, and it identified https://github.com/pytorch/pytorch/issues/74279.

A reference inputs function can be specified for an OpInfo by filling in its "reference_inputs_func" metadata. If this is done it's recommended that the reference inputs function first call the sample inputs function, then produce additional sample inputs. See `reference_inputs_elementwise_binary` for an example of this pattern.

In addition to implementing reference inputs for the elementwise binary operations, this PR improves their consistency and simplifies how their metadata is represented. The great majority now use a generic sample input function, and those that want extensions start by calling the generic sample input function and then adding additional samples. This removes many older sample input functions. The BinaryUfuncInfo subclass also now allows specifying scalar support more precisely, and reference inputs and error inputs are generated based on this metadata to ensure it's correct.

cc @kshitij12345 @pmeier @zou3519 @Chillee

Pull Request resolved: https://github.com/pytorch/pytorch/pull/74280
Approved by: https://github.com/ngimel
 2022-03-21 03:24:16 +00:00
Digant Desai
b2054d3025 Prepare for an update to the XNNPACK submodule (#72642) 
Summary:
- Target Sha1: ae108ef49aa5623b896fc93d4298c49d1750d9ba
- Make USE_XNNPACK a dependent option on cmake minimum version 3.12
- Print USE_XNNPACK under cmake options summary, and print the
  availability from collet_env.py
- Skip XNNPACK based tests when XNNPACK is not available
    - Add SkipIfNoXNNPACK wrapper to skip tests
- Update cmake version for xenial-py3.7-gcc5.4 image to 3.12.4
    - This is required for the backwards compatibility test.
      The PyTorch op schema is XNNPACK dependent. See,
      aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp for
      example. The nightly version is assumed to have USE_XNNPACK=ON,
      so with this change we ensure that the test build can also
      have XNNPACK.
- HACK: skipping test_xnnpack_integration tests on ROCM

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72642

Reviewed By: kimishpatel

Differential Revision: D34456794

Pulled By: digantdesai

fbshipit-source-id: 85dbfe0211de7846d8a84321b14fdb061cd6c037
(cherry picked from commit 6cf48e7b64d6979962d701b5d493998262cc8bfa)
 2022-02-25 00:39:15 +00:00
Philip Meier
b5f2574f36 no longer coalesce sparse COO tensors before comparison (#69751) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69751

cc nikitaved pearu cpuhrsch IvanYashchuk

Test Plan: Imported from OSS

Reviewed By: zou3519

Differential Revision: D34262453

Pulled By: ezyang

fbshipit-source-id: e2e62d2aa03fc569d2951c880960b256f5dc4aaa
(cherry picked from commit cb6b0ef719)
 2022-02-17 02:33:08 +00:00
Kurt Mohler
8e7fe87630 Rename Typed/UntypedStorage to _Typed/_UntypedStorage (#72540) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72540

Reviewed By: jbschlosser

Differential Revision: D34216823

Pulled By: bdhirsh

fbshipit-source-id: 1bc9930ab582771ebf02308e035576cd1a0dbe47
(cherry picked from commit 329238f612)
 2022-02-15 23:53:01 +00:00
Nikita Shulga
38ebb776a4 Fail with unexpected success for fatal errors (#72016) 
Summary:
Rest of the tests from CUDA testuite is skipped after GPU context corruption is encountered.
For tests decorated with `expectedFailure` creates false impression that entire testsuite is passing.
Remedy it by suppressing the exception and printing the warning about unexpected success if `should_stop_early` is true
Also, prints warning when this happens (to make attribution easier) as well as when this condition is detected.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72016

Test Plan:
`python test_ops.py -v  -k test_fn_fwgrad_bwgrad_gradient`
Before the change:
```
test_fn_fwgrad_bwgrad_gradient_cpu_complex128 (__main__.TestGradientsCPU) ... ok
test_fn_fwgrad_bwgrad_gradient_cpu_float64 (__main__.TestGradientsCPU) ... ok
test_fn_fwgrad_bwgrad_gradient_cuda_complex128 (__main__.TestGradientsCUDA) ... expected failure

----------------------------------------------------------------------
Ran 3 tests in 0.585s
OK (expected failures=1)
```

After the change:
```
test_fn_fwgrad_bwgrad_gradient_cpu_complex128 (__main__.TestGradientsCPU) ... ok
test_fn_fwgrad_bwgrad_gradient_cpu_float64 (__main__.TestGradientsCPU) ... ok
test_fn_fwgrad_bwgrad_gradient_cuda_complex128 (__main__.TestGradientsCUDA) ... /home/conda/miniconda3/lib/python3.9/site-packages/torch/testing/_internal/common_utils.py:1670: UserWarning: TEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failed with CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
  warn(f"TEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failed with {rte}")
/home/conda/miniconda3/lib/python3.9/site-packages/torch/testing/_internal/common_device_type.py:382: UserWarning: Suppressed expected failure that resulted in fatal error
  warn("Suppressed expected failure that resulted in fatal error")
unexpected success

----------------------------------------------------------------------
Ran 3 tests in 0.595s

FAILED (unexpected successes=1)
```
And `stderr` from XML file contains requested info:
```
/home/conda/miniconda3/lib/python3.9/site-packages/torch/testing/_internal/common_utils.py:1670: UserWarning: TEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failed with CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
  warn(f"TEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failed with {rte}")
/home/conda/miniconda3/lib/python3.9/site-packages/torch/testing/_internal/common_device_type.py:382: UserWarning: Suppressed expected failure that resulted in fatal error
  warn("Suppressed expected failure that resulted in fatal error")
```

Fixes https://github.com/pytorch/pytorch/issues/71973

Reviewed By: janeyx99, ngimel

Differential Revision: D33854287

Pulled By: malfet

fbshipit-source-id: dd0f5a4d2fcd21ebb7ee50ce4ec4914405a812d0
(cherry picked from commit 0c0baf3931)
 2022-02-03 17:49:59 +00:00
lezcano
6cb128c8dd Generalize noncontiguous tests to several outputs (#67996) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67996

This is necessary for most matrix decompositions in `linalg`.

cc mruberry

Test Plan: Imported from OSS

Reviewed By: anjali411

Differential Revision: D33774418

Pulled By: mruberry

fbshipit-source-id: 576f2dda9d484808b4acf0621514c0ffe26834e6
(cherry picked from commit fb07c50aa9)
 2022-01-27 23:13:17 +00:00
lezcano
8ff1a8fdca Implement forward AD for linalg.svd and improve svd_backward (#70253) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70253

I included a derivation of the formula in the complex case, as it is
particularly tricky. As far as I know, this is the first time this formula
is derived in the literature.

I also implemented a more efficient and more accurate version of svd_backward.
More importantly, I also added a lax check in the complex case making sure the loss
function just depends on the subspaces spanned by the pairs of singular
vectors, and not their joint phase.

cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano

Test Plan: Imported from OSS

Reviewed By: mikaylagawarecki

Differential Revision: D33751982

Pulled By: mruberry

fbshipit-source-id: c2a4a92a921a732357e99c01ccb563813b1af512
(cherry picked from commit 391319ed8f)
 2022-01-27 18:38:30 +00:00
Philip Meier
d4d0ab71b3 use torch.testing.assert_equal in TestCase.assertEqual (#67796) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67796

Supersedes #58981.

cc mruberry

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D33542994

Pulled By: mruberry

fbshipit-source-id: 527099f5fdc154fd95ee48cd19f0a85eeec43443
(cherry picked from commit 1a58915e2c)
 2022-01-27 08:33:55 +00:00
Khushi Agrawal
dfcbe059ec Obliviate ALL_TENSORTYPES and ALL_TENSORTYPES2. (#71153) 
Summary:
Hi,
The PR fixes https://github.com/pytorch/pytorch/issues/71096.  It aims to scan all the test files and replace ` ALL_TENSORTYPES` and `ALL_TENSORTYPES2` with `get_all_fp_dtypes`.

I'm looking forward to your viewpoints!

Thanks!

cc: janeyx99 kshitij12345

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71153

Reviewed By: jbschlosser, mruberry

Differential Revision: D33533346

Pulled By: anjali411

fbshipit-source-id: 75e79ca2756c1ddaf0e7e0289257fca183a570b3
(cherry picked from commit da54b54dc5)
 2022-01-26 03:25:02 +00:00
Jane Xu
506d41d659 Improve disable name match (#71499) 
Summary:
Allows disabling issues to disable all parametrized tests with dtypes.

Tested locally with:
1. .pytorch-disabled-tests.json as
```
{"test_bitwise_ops (__main__.TestBinaryUfuncs)": ["https://github.com/pytorch/pytorch/issues/99999", ["mac"]]}
```
and running `python test_binary_ufuncs.py --import-disabled-tests -k test_bitwise_ops` yields all tests skipped.

2. .pytorch-disabled-tests.json as
```
{"test_bitwise_ops_cpu_int16 (__main__.TestBinaryUfuncsCPU)": ["https://github.com/pytorch/pytorch/issues/99999", ["mac"]]}
```
and running `python test_binary_ufuncs.py --import-disabled-tests -k test_bitwise_ops` yields only `test_bitwise_ops_cpu_int16` skipped.

NOTE: this only works with dtype parametrization, not all prefixes, e.g., disabling `test_async_script` would NOT disable `test_async_script_capture`. This is the most intuitive behavior, I believe, but I can be convinced otherwise.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71499

Reviewed By: mruberry

Differential Revision: D33742723

Pulled By: janeyx99

fbshipit-source-id: 98a84f9e80402978fa8d22e0f018e6c6c4339a72
(cherry picked from commit 3f778919ca)
 2022-01-25 01:30:02 +00:00
Mike Ruberry
e0d829a266 Kill the test_torch.py mixin and creates test_scatter_gather_ops (#71691) 
Summary:
Per title.

Also annotates test_torch.py with additional cleanup tasks and adds empty sample inputs to elementwise unary and binary OpInfos.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71691

Reviewed By: ngimel

Differential Revision: D33735126

Pulled By: mruberry

fbshipit-source-id: 8cc097a7581a8b620540c95b2a5889c1165ecf23
(cherry picked from commit 5c6a245a3f)
 2022-01-24 09:32:32 +00:00
John Clow
e4d522a3cf More informative messages for None types comparisons (#69802) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69802

Test Plan: Imported from OSS

Reviewed By: anjali411

Differential Revision: D33555886

Pulled By: Gamrix

fbshipit-source-id: 3045cbe04de22f05db41a99ad3dda90c5271aa0f
 2022-01-13 13:59:28 -08:00
John Clow
03c4d2b9e3 Adding support for Ifs in Device Type Analysis (#69050) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69050

Test Plan: Imported from OSS

Reviewed By: anjali411

Differential Revision: D33555887

Pulled By: Gamrix

fbshipit-source-id: f7f057c5985f8b6e7a9fe5702a944b2b4cc4d5b5
 2022-01-13 13:59:18 -08:00
Jane Xu
c4400fc431 Retire repeat_test_for_types (#71033) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/69865

cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71033

Reviewed By: mruberry

Differential Revision: D33486370

Pulled By: janeyx99

fbshipit-source-id: 71f9383dbc1e00b572f26eb4f04d0a94c6759e35
 2022-01-10 09:13:54 -08:00
lezcano
baeca11a21 Remove random_fullrank_matrix_distinc_singular_value (#68183) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68183

We do so in favour of
`make_fullrank_matrices_with_distinct_singular_values` as this latter
one not only has an even longer name, but also generates inputs
correctly for them to work with the PR that tests noncontig inputs
latter in this stack.

We also heavily simplified the generation of samples for the SVD, as it was
fairly convoluted and it was not generating the inputs correclty for
the noncontiguous test.

To do the transition, we also needed to fix the following issue, as it was popping
up in the tests:

Fixes https://github.com/pytorch/pytorch/issues/66856

cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D32684853

Pulled By: mruberry

fbshipit-source-id: e88189c8b67dbf592eccdabaf2aa6d2e2f7b95a4
 2022-01-05 20:33:37 -08:00
lezcano
a35b4b49d2 Add linalg.lu_factor (#66933) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66933

This PR exposes `torch.lu` as `torch.linalg.lu_factor` and
`torch.linalg.lu_factor_ex`.

This PR also adds support for matrices with zero elements both in
the size of the matrix and the batch. Note that this function simply
returns empty tensors of the correct size in this case.

We add a test and an OpInfo for the new function.

This PR also adds documentation for this new function in line of
the documentation in the rest of `torch.linalg`.

Fixes https://github.com/pytorch/pytorch/issues/56590
Fixes https://github.com/pytorch/pytorch/issues/64014

cc jianyuh nikitaved pearu mruberry walterddr IvanYashchuk xwang233 Lezcano

Test Plan: Imported from OSS

Reviewed By: gchanan

Differential Revision: D32834069

Pulled By: mruberry

fbshipit-source-id: 51ef12535fa91d292f419acf83b800b86ee9c7eb
 2022-01-05 20:32:12 -08:00
Pearu Peterson
2292520bdc Fix genSparseCSRTensor: generate non-trivial values for uint8 dtype. (#70580) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70580

cc nikitaved pearu cpuhrsch

Test Plan: Imported from OSS

Reviewed By: jbschlosser

Differential Revision: D33413597

Pulled By: cpuhrsch

fbshipit-source-id: 313b08e1bd96ffb8d5c7a0fda9384502325e5d08
 2022-01-04 18:02:36 -08:00
Mike Ruberry
84b7832010 Updates CUDA memory leak check to verify against driver API and print more diagnostic information (#69556) 
Summary:
Per title

Pull Request resolved: https://github.com/pytorch/pytorch/pull/69556

Reviewed By: mrshenli

Differential Revision: D32954770

Pulled By: mruberry

fbshipit-source-id: a6c2ae6f704422c178569980ca4b9c72c4272f55
 2021-12-17 23:37:49 -08:00
Peter Bell
6de9f0fc94 OpInfo: Allow sample_inputs_func to be any iterable (#69256) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69256

Closes #52486

Test Plan: Imported from OSS

Reviewed By: mrshenli

Differential Revision: D32942008

Pulled By: mruberry

fbshipit-source-id: f5b01b0298c0160b0bec6e86e2b6db8cfe746206
 2021-12-09 08:37:26 -08:00